A. Field of the Invention
The present invention is related to methods for the detection of breast cancer with screening mammography and more particularly to a method for the detection and classification of microcalcification clusters in digital mammograms that uses neural networks and genetic algorithms processed by a data processing system.
B. Description of the Related Art
Cancer is a term used to refer to a group of diseases where a group of cells of the body grow, change and multiply out of control. Usually, each type of cancer is named after the body part where it originated. When this erratic and uncontrolled proliferation of cells occurs in the breast tissues, it is known as breast cancer. Breast cancer is the fifth cause of death caused by cancer worldwide, after lung cancer, stomach cancer, liver cancer and colon cancer. During 2005, breast cancer caused approximately 502,000 deaths in the world. Among women, breast cancer is the type of cancer that causes the largest number or deaths worldwide, followed by lung, stomach, colorectal and cervical cancers
The highest survival rates for breast cancer occur when it is detected in its earlier stages, when it usually appears in mammograms as very small specks of calcium known as microcalcifications. This survival rate decreases as cancer progresses undetected forming a mass or lump, called a tumor (extra tissue formed by rapidly dividing cells). Tumors can be either malignant (cancerous) or benign (non-cancerous). Breast malignant tumors penetrate and destroy healthy breast tissues. Eventually, a group of cells from a tumor may break away and spread to other parts of the body. These groups of cells spreading to another region are called metastases. Survival rates when breast cancer is discovered and begins to be treated in these advanced stages are low. There are several techniques for discovering breast cancer, which vary in their invasiveness, detection effectiveness and the breast cancer stage where they are more effective in. None of them provides absolute certainty about their predictions, so false positives (declared positive when no cancer is present) or false negatives (declared negative when cancer is present) results may occur, at different degrees of frequency. Breast self-examination (BSE) and clinical breast exam (CBE, performed by a trained medical or health professional) are the easiest procedures for detecting breast cancer, and they can detect lumps that are likely to be of cancerous origin. Nevertheless, if a detected lump is in fact malign, it means it has been developing for sometime, and certainly it is not in its initial stage. Other non-invasive methods that can detect breast cancer in early stages are mammography and breast ultrasonography.
Mammography is a special type of x-ray imaging used to create detailed images of the breast, and is the most widely used method for breast cancer detection in its early stages. Mammography can show changes in the breast well before a woman or her physician can feel them. Once a lump is discovered, mammography can be very useful in evaluating the lump to determine if it is cancerous. If a breast abnormality is found or confirmed with mammography, additional breast imaging tests such as ultrasound (sonography) or a breast biopsy may be performed. A biopsy is an invasive procedure, and it involves taking one or more samples of breast tissue and examining it under a microscope to determine whether it contains cancer cells or not. Usually, mammography or ultrasound, are used to help the radiologist or surgeon guide the needle to the correct area in the breast during biopsy. In resume, the main motivation of this work is the need to count with efficient tools that analyze the results of common techniques used in early detection of breast cancer (mainly mammograms) in order to support the work of expert radiologists and help them to provide more accurate and earlier results to patients, and therefore increasing their chances to survive.
Despite many advances in techniques for early breast cancer detection, mammography is still the main standard, especially in developing countries. Its low cost, and the fact that is not invasive, coupled with its high effectiveness make it a widely used technique. Nevertheless, a small percentage of cancer can be missed by mammography, and it is still important for women to have their breasts examined on a regular basis by a healthcare professional, and perform monthly breast self-exams. There are several findings that can be observed in a mammogram, like masses, cysts, architectural distortions, areas with asymmetric densities, and microcalcifications (tiny calcium deposits).
Microcalcifications are often signs of breast cancer in its earliest stages, especially when they appear forming clusters. It is a common practice for radiologists who diagnose early signs of breast cancer in mammograms to pay special attention in the detection of microcalcification clusters. However, the predictive value of mammograms is relatively low, compared to biopsy. This low sensitivity (correct diagnosis of positive cases) is caused by the low contrast between the cancerous tissue and the normal parenchymal tissue, the small size of microcalcifications and possible deficiencies in the image digitalization process. The sensitivity may be improved having each mammogram checked by two or more radiologists.
While a radiologist may miss some abnormalities in a case, another specialist may detect them. Despite the obvious benefit of double checking, it has the consequence of making the process inefficient from a practical viewpoint, because of the usually reduced number of mammography specialists in medical institutions (specially in developing countries) and by reducing the individual productivity of those specialists. The process of producing a mammogram may take between 20 to 30 minutes, and an initial diagnosis, usually takes about 40 minutes. Said time could increase if the suspicious abnormalities cannot be easily identified and confirmed as benign. A viable alternative is to replace one of the radiologists by a computer system, giving a second opinion. The system could emphasize suspicious abnormalities and show regions of interest for the specialists, and the results provided by the system could be confirmed or rejected by them.
In general, any computer system intended for the detection and diagnosis of individual microcalcifications and microcalcification clusters in mammograms should have some image processing functions that make possible the identification and isolation of individual microcalcifications and the posterior identification of microcalcification clusters. Such system should also include some classifying techniques for pre-diagnosing the detected microcalcification clusters into benign or malignant. Some previous works have used techniques like wavelets, fractal models, support vector machines, mathematical morphology, bayesian image analysis models, high order statistic, fuzzy logic, etc., in order to attempt the detection of real microcalcifications in the mammogram.
The present invention comprises a method for detecting microcalcification clusters in mammograms; and their classification into one of two classes: benign (usually the presence of tiny benign cysts) or malignant (possible presence of early breast cancer). This procedure is mainly based in difference of Gaussian (DoG) filters for the detection of suspicious objects in a mammogram, and artificial intelligence techniques combining genetic algorithms (GA) and evolutionary artificial neural networks (ANN) for the classification of such objects into microcalcifications or non-microcalcifications, and later for classifying the detected microcalcification clusters into benign or malignant.
DoG filters are adequate for the noise-invariant and size-specific detection of spots, like the points that appear in a DoG image. This DoG image represents the microcalcifications if a thresholding operation is applied to it. A procedure that applies a sequence of difference of Gaussian filters was developed, in order to maximize the amount of detected probable individual microcalcifications (signals) in the mammogram, which are later classified in order to detect if they are real microcalcifications or not. Finally, microcalcification clusters are identified and also classified to determine which ones are malignant and which ones are benign using several types of evolutionary neural networks.